Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”

Autores
López Correa, Juan Manuel; Moreno, Hugo; Pérez, Diego Sebastián; Bromberg, Facundo; Andújar, Dionisio
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.
Fil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Consejo Superior de Investigaciones Científicas; España
Fil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; España
Fil: Pérez, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; España
Materia
Weed Management
Weeds Species Classification
Zero Tillage
Computer Vision Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/256386

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spelling Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”López Correa, Juan ManuelMoreno, HugoPérez, Diego SebastiánBromberg, FacundoAndújar, DionisioWeed ManagementWeeds Species ClassificationZero TillageComputer Vision Machine Learninghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.Fil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Consejo Superior de Investigaciones Científicas; EspañaFil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; EspañaFil: Pérez, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Bromberg, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; EspañaElsevier2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/256386López Correa, Juan Manuel; Moreno, Hugo; Pérez, Diego Sebastián; Bromberg, Facundo; Andújar, Dionisio; Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”; Elsevier; Computers and Eletronics in Agriculture; 217; 108576; 2-2024; 1-130168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S016816992300964Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2023.108576info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:09:01Zoai:ri.conicet.gov.ar:11336/256386instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:09:02.192CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
title Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
spellingShingle Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
López Correa, Juan Manuel
Weed Management
Weeds Species Classification
Zero Tillage
Computer Vision Machine Learning
title_short Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
title_full Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
title_fullStr Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
title_full_unstemmed Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
title_sort Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”
dc.creator.none.fl_str_mv López Correa, Juan Manuel
Moreno, Hugo
Pérez, Diego Sebastián
Bromberg, Facundo
Andújar, Dionisio
author López Correa, Juan Manuel
author_facet López Correa, Juan Manuel
Moreno, Hugo
Pérez, Diego Sebastián
Bromberg, Facundo
Andújar, Dionisio
author_role author
author2 Moreno, Hugo
Pérez, Diego Sebastián
Bromberg, Facundo
Andújar, Dionisio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Weed Management
Weeds Species Classification
Zero Tillage
Computer Vision Machine Learning
topic Weed Management
Weeds Species Classification
Zero Tillage
Computer Vision Machine Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.
Fil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Consejo Superior de Investigaciones Científicas; España
Fil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; España
Fil: Pérez, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; España
description The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/256386
López Correa, Juan Manuel; Moreno, Hugo; Pérez, Diego Sebastián; Bromberg, Facundo; Andújar, Dionisio; Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”; Elsevier; Computers and Eletronics in Agriculture; 217; 108576; 2-2024; 1-13
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/256386
identifier_str_mv López Correa, Juan Manuel; Moreno, Hugo; Pérez, Diego Sebastián; Bromberg, Facundo; Andújar, Dionisio; Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance”; Elsevier; Computers and Eletronics in Agriculture; 217; 108576; 2-2024; 1-13
0168-1699
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S016816992300964X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2023.108576
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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